Oral Sucrose is an Effective and Safe Analgesic for Painful Minor Procedures in Infants during Primary Health Care Visit

Pain induced by minor office procedures are associated with infant and family’s distress with possible long term psychological effects. Despite this known fact, it is not adequately treated in common practice. American Academy of Pediatrics (AAP) recommends pre-procedural oral sucrose to alleviate pain during the procedures. The purpose of this study was to review published literature for the efficacy and safety of oral sucrose as a pre-procedural intervention in infants for mild to moderate procedural pain. A PUBMED, MEDLINE and COCHRANE database search was performed using the terms analgesia, infant, neonatal, newborn, nociception, pain, sucrose and randomized controlled study. Thirteen studies were selected for review after the exclusion criteria. The studies were reviewed for the outcome measures reported including, 1) efficacy of a single oral dose of sucrose as determined by pain scores, behavioral and physiological indicators and, 2) adverse events reported and safety. Furthermore, some other interventions outcomes were also reviewed including the dose, concentration of solution, timing and method of delivery of oral sucrose. Oral sucrose is effective in reducing crying time and decreasing behavioral pain responses when given in a single dose 30 seconds to 2 minutes before the procedure in 10 out of 13 studies. No clinical significant adverse event was reported in 12 out of 13 studies. In conclusion, oral sucrose is an effective, safe, and immediate acting analgesic which reduces crying time and behavioral pain responses after minor painful procedures in infants. This literature review of high quality studies supports the AAP recommendation of using pre-procedural oral sucrose for pain control in infants during office procedures.https://commons.und.edu/pas-grad-posters/1138/thumbnail.jp

Lipid transporters and receptor in salmon louse (Lepeophtheirus salmonis) : Effect of RNAi Knockdown on oogenesis, embryonal development and larval maturation

The salmon louse, Lepeophtheirus salmonis, is a marine ectoparasite of salmonids in the Northern Hemisphere. At present, salmon louse infestation is considered as one of the biggest challenges in the salmon farming industry, causing huge economic losses, and also considered a threat to wild populations of salmonids. Control of salmon lice on farmed salmon has mainly depended on the use of chemotherapeutants. However, over the past few years, the salmon louse has developed resistance against most available chemicals. As a consequence, non-chemical treatment methods such as cleaner fish have been introduced in salmon farming, but the production, health and welfare in the cleaner fish have been challenging. It is, therefore, evident that new treatment methods are needed to control this parasite. For this purpose, further understanding of the biology of this parasite is crucial to identify new principles or drug targets. Lipids are an important source of energy for the growth and reproduction of animals. Other functions include their role in cellular signalling and as structural components in the cell membranes. In oviparous animals, females deposit lipids to maturing eggs to be utilized during embryogenesis and larval development. Transport of lipids through the circulation of animals to developing oocytes is facilitated by lipoproteins, which consist of lipids and protein components known as apolipoproteins. Lipoproteins carry lipids from the site of synthesis or storage to the site of utilization/storage while lipoprotein receptors facilitate uptake of lipoproteins. Previous studies in vertebrates and some insects showed that maturation of these lipoproteins is under the control of another protein known as microsomal triglyceride transfer protein (MTP). Female salmon lice produce large numbers of lipid-enriched eggs throughout its life span. Similar to other oviparous animals, female louse accumulates a large amount of lipids in developing eggs during vitellogenesis. In female salmon lice, transport of maternal lipids to growing oocytes of female lice has not been addressed before. Presence of genes encoding MTP, apolipoproteins (apoLps) and lipophorin receptor (LpR) may suggest a similar mechanism of lipid metabolism/transport as found in other organisms. Lipoproteins require for extracellular transport of lipids to different tissues of animals and assembly, as well as secretion of these lipoproteins depend upon MTP. In oviparous species, female supply enough lipids to oocytes to secure successful embryogenesis and early larval development. It is likely that female salmon lice use similar lipoprotein based mechanism to supply maternal lipids to growing oocytes. Therefore it is important to study the role of MTP in the supply of lipids to growing oocytes. Three transcript variants of MTP were found in the salmon louse and all variants transcribed differently in different tissues of an adult female. Functional studies conducted through RNAi induced transcript knock down confirmed that female lice produce offspring with very low lipid contents and survival rate of 10-30% compare to control group animals. The present study suggests that MTP has an important function in reproduction and lipid metabolism in salmon louse and may be considered in the development of a new anti-parasitic treatment method. Protein components of lipoproteins, apoLPs, are essential in the transport of lipids to different tissues of animals through their interaction with cell surface lipoprotein receptors. Similar to other oviparous animals, it is possible that female salmon louse use lipoproteins for the transport of maternal lipids to growing oocytes where apoLps of lipoproteins bind with lipoprotein receptors and release lipids to the oocytes. In salmon lice, two apoLps encoding genes (LsLp1 and LsLp2) were identified. Expression of both genes was found in the intestine and sub-cuticular tissue of adult female louse. RNAi mediated-knockdown of both genes in female louse confirmed significant reduction of transcripts levels. Female lice injected with LsLp1 double-stranded RNA produced short egg-strings as well as significantly fewer offspring compared to control lice. Knockdown of LsLp2 did not show any effect on the eggstring production and numbers of offspring compared with control lice. Functional studies were conducted through RNAi suggested that LsLp1 play an important role in reproduction of female lice. Previous studies in different organisms show that members of low-density lipoprotein receptor (LDLR) superfamily mediate the endocytosis of lipoproteins. In salmon louse genome database, single gene homologous to insect lipophorin receptor was identified and named as L. salmonis lipophorin receptor (LsLpR). The LsLpR consists of 16 exons and encodes a protein of 952 amino acids. Structural analysis showed that the predicted structure of LsLpR contains five functional domains similar to LpR of insects. Phylogenetic analysis placed LsLpR together with LpR of insects. The highest abundance of LsLR transcripts was found in copepodids and adult females. In adult females, receptor transcripts and proteins were found in the ovary and vitellogenic oocytes. While in larvae, the LsLpR transcripts were found in the neuronal somata of the brain and in the intestine. Possible functions of LsLpR in reproduction and lipid metabolism were investigated through RNA interference. Knockdown in larvae decreased the transcription of LsLpR by 44-54%, and knockdown of LsLpR in adult female lice reduced the number of offspring by 72% compared with control lice.Doktorgradsavhandlin

Trust and Believe -- Should We? Evaluating the Trustworthiness of Twitter Users

Social networking and micro-blogging services, such as Twitter, play an important role in sharing digital information. Despite the popularity and usefulness of social media, they are regularly abused by corrupt users. One of these nefarious activities is so-called fake news -- a "virus" that has been spreading rapidly thanks to the hospitable environment provided by social media platforms. The extensive spread of fake news is now becoming a major problem with far-reaching negative repercussions on both individuals and society. Hence, the identification of fake news on social media is a problem of utmost importance that has attracted the interest not only of the research community but most of the big players on both sides - such as Facebook, on the industry side, and political parties on the societal one. In this work, we create a model through which we hope to be able to offer a solution that will instill trust in social network communities. Our model analyses the behaviour of 50,000 politicians on Twitter and assigns an influence score for each evaluated user based on several collected and analysed features and attributes. Next, we classify political Twitter users as either trustworthy or untrustworthy using random forest and support vector machine classifiers. An active learning model has been used to classify any unlabeled ambiguous records from our dataset. Finally, to measure the performance of the proposed model, we used accuracy as the main evaluation metric.Comment: arXiv admin note: text overlap with arXiv:2107.0802

Learning in the Dark: Privacy-Preserving Machine Learning using Function Approximation

Over the past few years, a tremendous growth of machine learning was brought about by a significant increase in adoption and implementation of cloud-based services. As a result, various solutions have been proposed in which the machine learning models run on a remote cloud provider and not locally on a user's machine. However, when such a model is deployed on an untrusted cloud provider, it is of vital importance that the users' privacy is preserved. To this end, we propose Learning in the Dark -- a hybrid machine learning model in which the training phase occurs in plaintext data, but the classification of the users' inputs is performed directly on homomorphically encrypted ciphertexts. To make our construction compatible with homomorphic encryption, we approximate the ReLU and Sigmoid activation functions using low-degree Chebyshev polynomials. This allowed us to build Learning in the Dark -- a privacy-preserving machine learning model that can classify encrypted images with high accuracy. Learning in the Dark preserves users' privacy since it is capable of performing high accuracy predictions by performing computations directly on encrypted data. In addition to that, the output of Learning in the Dark is generated in a blind and therefore privacy-preserving way by utilizing the properties of homomorphic encryption.https://gitlab.com/nisec/blind_fait

Psychological Dimensions of Personality on Padmashree and Former Indian Hockey Captain Zafar Iqbal

The purpose of the present study is to highlight the valuable contribution of Mr. Zafar Iqbal for winning the last gold medal for India in the 1980 Moscow Olympics. He led the Indian team as a captain in various international events, prominent among them were Asian Games in 1982, Champions trophy, 1983, and 1984 Los Angeles Olympics. He was honoured by carrying the Indian flag at the youth festival held in Moscow, and later at the opening ceremony of the Los Angeles Olympics. He received the prestigious Arjuna Award in 1983, the highest award given to a sports personality in India. Mr. Zafar Iqbal was recruited as subjects of the study. To find out the score of Arjuna Awardee Zafar Iqbal on Neuroticism, Extraversion, Openness, Agreeableness and Conscientiousness. The NEO five-factor inventory scale was developed by Costa and McCrae (1991). Results have revealed that Mr. Zafar Iqbal scored average on neuroticism, extraversion and openness dimensions, and low on agreeableness and high on conscientiousness

A More Secure Split: Enhancing the Security of Privacy-Preserving Split Learning

Split learning (SL) is a new collaborative learning technique that allows participants, e.g. a client and a server, to train machine learning models without the client sharing raw data. In this setting, the client initially applies its part of the machine learning model on the raw data to generate Activation Maps (AMs) and then sends them to the server to continue the training process. Previous works in the field demonstrated that reconstructing AMs could result in privacy leakage of client data. In addition to that, existing mitigation techniques that overcome the privacy leakage of SL prove to be significantly worse in terms of accuracy. In this paper, we improve upon previous works by constructing a protocol based on U-shaped SL that can operate on homomorphically encrypted data. More precisely, in our approach, the client applies homomorphic encryption on the AMs before sending them to the server, thus protecting user privacy. This is an important improvement that reduces privacy leakage in comparison to other SL-based works. Finally, our results show that, with the optimum set of parameters, training with HE data in the U-shaped SL setting only reduces accuracy by 2.65% compared to training on plaintext. In addition, raw training data privacy is preserved.https://github.com/khoaguin/HESplitNe

Split Ways: Privacy-Preserving Training of Encrypted Data Using Split Learning

Split Learning (SL) is a new collaborative learning technique that allows participants, e.g. a client and a server, to train machine learning models without the client sharing raw data. In this setting, the client initially applies its part of the machine learning model on the raw data to generate activation maps and then sends them to the server to continue the training process. Previous works in the field demonstrated that reconstructing activation maps could result in privacy leakage of client data. In addition to that, existing mitigation techniques that overcome the privacy leakage of SL prove to be significantly worse in terms of accuracy. In this paper, we improve upon previous works by constructing a protocol based on U-shaped SL that can operate on homomorphically encrypted data. More precisely, in our approach, the client applies Homomorphic Encryption (HE) on the activation maps before sending them to the server, thus protecting user privacy. This is an important improvement that reduces privacy leakage in comparison to other SL-based works. Finally, our results show that, with the optimum set of parameters, training with HE data in the U-shaped SL setting only reduces accuracy by 2.65% compared to training on plaintext. In addition, raw training data privacy is preserved

A More Secure Split: Enhancing the Security of Privacy-Preserving Split Learning

Split learning (SL) is a new collaborative learning technique that allows participants, e.g. a client and a server, to train machine learning models without the client sharing raw data. In this setting, the client initially applies its part of the machine learning model on the raw data to generate Activation Maps (AMs) and then sends them to the server to continue the training process. Previous works in the field demonstrated that reconstructing AMs could result in privacy leakage of client data. In addition to that, existing mitigation techniques that overcome the privacy leakage of SL prove to be significantly worse in terms of accuracy. In this paper, we improve upon previous works by constructing a protocol based on U-shaped SL that can operate on homomorphically encrypted data. More precisely, in our approach, the client applies homomorphic encryption on the AMs before sending them to the server, thus protecting user privacy. This is an important improvement that reduces privacy leakage in comparison to other SL-based works. Finally, our results show that, with the optimum set of parameters, training with HE data in the U-shaped SL setting only reduces accuracy by 2.65% compared to training on plaintext. In addition, raw training data privacy is preserved.Comment: arXiv admin note: substantial text overlap with arXiv:2301.0877

Split Without a Leak: Reducing Privacy Leakage in Split Learning

Abstract. The popularity of Deep Learning (DL) makes the privacy of sensitive data more imperative than ever. As a result, various privacy-preserving techniques have been implemented to preserve user data privacy in DL. Among various privacy-preserving techniques, collaborative learning techniques, such as Split Learning (SL) have been utilized to accelerate the learning and prediction process. Initially, SL was considered a promising approach to data privacy. However, subsequent research has demonstrated that SL is susceptible to many types of attacks and, therefore, it cannot serve as a privacy-preserving technique. Meanwhile, countermeasures using a combination of SL and encryption have also been introduced to achieve privacy-preserving deep learning. In this work, we propose a hybrid approach using SL and Homomorphic Encryption (HE). The idea behind it is that the client encrypts the activation map (the output of the split layer between the client and the server) before sending it to the server. Hence, during both forward and backward propagation, the server cannot reconstruct the client’s input data from the intermediate activation map. This improvement is important as it reduces privacy leakage compared to other SL-based works, where the server can gain valuable information about the client’s input. In addition, on the MITBIH dataset, our proposed hybrid approach using SL and HE yields faster training time (about 6 times) and significantly reduced communication overhead (almost 160 times) compared to other HE-based approaches, thereby offering improved privacy protection for sensitive data in DL.https://github.com/khoaguin/HESplitNe